Imagine building a treehouse with a pile of random lumber, bolts, and screws. Before we can build, we need to organize our materials. Similarly, data needs to be prepared before it can be used for analysis, models, and reports. This is even more true when the size of the data is more comparable to a high-rise building than a treehouse project. This article will help you understand the data preparation process, how it is used in data projects, and how improving the process enables data analysts to spend time on higher-value work. Data preparation is the work done to take raw data and organize it to be analyzed. Whether for analytics, machine learning, or generative AI (GenAI), this process is a critical step that can be difficult and time consuming. Surveys show that over 60% of a data scie

VIEW ON DATAIKU.COM